Practical Scheduling Research Articles

Restless Video Bandits: Optimal SVC Streaming in a Multi-User Wireless Network

In this paper, we consider the problem of optimal scalable video delivery to mobile users in wireless networks given arbitrary Quality Adaptation (QA) mechanisms. In current practical systems, QA and wireless channel scheduling are performed independently by the content provider and network operator, respectively. While most research has been focused on jointly optimizing these two tasks, the high complexity that comes with a joint approach makes the implementation impractical. Therefore, we present a scheduling mechanism that takes the QA logic of each user as input and optimizes the scheduling accordingly. Hence, there is no need for centralized QA and cross-layer interactions are minimized. We model the QA-adaptive scheduling and the jointly optimal problem as a Restless Bandit and a Multi-user Semi Markov Decision Process, respectively, in order to compare the loss incurred by not employing a jointly optimal scheme. We then present heuristic algorithms in order to achieve the optimal outcome of the Restless Bandit solution, assuming the base station has knowledge of the underlying quality adaptation of each user (QA-Aware). We also present a simplified heuristic without the need for any knowledge of the QA logic at the base station (QA-Blind). We show that our QA-Aware strategy can achieve up to two times improvement in network utilization compared to popular baseline algorithms such as Proportional Fairness. We also show that the QA-Blind strategy performs very close to the QA-Aware scheme while providing significant reduction in complexity. Furthermore, we provide a testbed implementation of the QA-Blind scheme in order to compare it with baseline algorithms in a real network setting.

Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant

In the paper, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this paper involves many attributes, including working calendar, due dates, and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for the online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of the practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm, and improve the solution quality. To this end, three multi-agent-based hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for the online scheduling problem. To evaluate the performance of evolved SPs, a 5-fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent-based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multi-objective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded in the manufacturing execution system that is built on JAVA and successfully applied in several manufacturing plants.

Teaching surgical skills in a resource-limited setting: Comparing massed versus distributed practice in an ultrasound-guided breast biopsy simulator

Background: Teaching surgical skills in the simulation lab has increased markedly compared to teaching only in the operating room. Although many studies have been performed investigating the optimal teaching methodology for skills acquisition, there is no consensus on the best method. Massed and distributed practices are important methods in teaching procedural skills. Considering the limited human and logistical resources in low and middle-income settings, it is valuable to understand the optimal methodology for learning and acquiring surgical skills. Methods: Thirty-two core needle biopsy-naïve first-year residents and final year medical students rotating in general surgery were enrolled in and completed the study at University Teaching Hospital of Kigali, a tertiary, teaching and referral hospital in Kigali, Rwanda. They were assigned to a “massed” group (i.e., one time, 3-hour practice) or “distributed” group (i.e., 1-hour practice per week for 3 weeks). Trainees were taught ultrasound-guided core needle biopsy on a high-fidelity breast simulator. All participants completed pre- and post-tests and an evaluation of skill retention was performed one month after completion of the training. Analysis of performance was completed, and p-value ≤ 0.05 was considered statistically significant. Results: There was no difference between performance on the pretest (p=0.985) and the posttest (p=0.680). Both groups demonstrated improvement after implementation of the simulation training when comparing pretest and posttest results (p<0.001); there were no differences in the evaluation of skills retention after one month after the training between the two groups (p=0.273). Conclusions: The results of this study demonstrate that both groups have improved significantly their knowledge and skills. Trainees have similar retention of skills in ultrasound guided core needle biopsy on a breast simulator whether trained under a massed or distributed practice schedule. Both methods may be considered in our setting for teaching surgical skills. Keywords: surgical simulation; resource-limited setting; global surgery

Assessing the Impact of Reference Evapotranspiration Models on Decision Support Systems for Irrigation

Reference evapotranspiration (E T 0) is a major estimator for crop water requirements predicted by decision support systems for irrigation. However, the impact of different E T 0s on the predicted amount of water supply and counts of irrigation events has not been evaluated. Simulations of the Geisenheim Irrigation Scheduling (GS) for vegetable crops with two different E T 0s, P2-E T 0 and FAO56-E T 0, were evaluated to assess exemplarily the impact of E T 0s. The sensitivity of both E T 0s to local climate conditions was characterized through a random forest analysis, and a linear regression model was used to adjust the original GS by adapting K c-values to the exchange E T 0. For assessing the outcomes of GS irrigation decision, simulations of 173 individual cropping cycles including six vegetable crops over eight years were conducted. After adjusting P2-E T 0 K c-values to FAO56-E T 0 K c-values, there was no impact of the E T 0-model on the practical irrigation scheduling with GS. Finally, we discuss that any E T 0-model, if adjusted accordingly, might have little impact on similar irrigation systems and provide a method to exchange E T 0s.

Distributed Practice: Rarely Realized in Self-Regulated Mathematical Learning.

The purpose of the present study was to investigate the effect and use of distributed practice in the context of self-regulated mathematical learning in high school. With distributed practice, a fixed learning duration is spread over several sessions, whereas with massed practice, the same time is spent learning in one session. Distributed practice has been proven to be an effective tool for improving long-term retention of verbal material and simple procedural knowledge in mathematics, at least when the practice schedule is externally guided. In the present study, distributed practice was investigated in a context that required a higher degree of self-regulation. In total, 158 secondary school students were invited to participate. After motivational and cognitive characteristics of the students were assessed, the students were introduced to basic statistics, a topic of their regular curriculum. At the end of the introduction, the students could sign up for the study to further practice this content. Eighty-seven students did so and were randomly assigned either to the distributed or to the massed practice condition. In the distributed practice condition, they received three practice sets on three different days. In the massed practice condition, they received the same three sets, but all on one day. All exercises were worked in the context of self-regulated learning at home. Performance was tested 2 weeks after the last practice set. Only 44 students finished the study, which hampered the analysis of the effect of distributed practice. The characteristics of the students who completed the exercises were analyzed exploratory: The proportion of students who finished all exercises was significantly higher in the massed than in the distributed practice condition. Within the distributed practice condition, a significantly larger proportion of female students completed the exercises compared to male students. Additionally, among these female students, a larger proportion showed lower concentration difficulty. No such differential effects were revealed in the massed practice condition. Our results suggest that the use of distributed practice in the context of self-regulated learning might depend on learner characteristics. Accordingly, distributed practice might obtain more reliable effects in more externally guided learning contexts.

Influence of Annual Meetings of the American Society of Anesthesiologists and of Large National Surgical Societies on Caseloads of Major Therapeutic Procedures.

Statistical methods to calculate the hours into which cases are scheduled, based on minimizing the inefficiency of use of anesthesia and/or operating room (OR) time, assure that anesthesiologists and OR nurses are available on each day to care for patients while infrequently working late. The method assumes that anesthesia staffing is planned based on the workload and not vice-versa. We used the American Society of Anesthesiologists (ASA) meeting to test the assumption across a large population. The observational study cohort was all 3,191,282 major therapeutic procedures performed during 2,517,842 cases at all 121 non-federal hospitals in the State of Iowa, 2007-16. The Fridays to Wednesdays of the annual ASA meetings were compared pairwise by year with those days of the other (mean = 43.0 [SE =0.3]) weeks without a federal holiday. Differences in counts of procedures (P = 0.45, 0.5% [0.7%]) and cases performed (P = 0.93, 0.1% [1.0%]) were not significant. In contrast, compared to non-meeting weeks, during the American College of Surgeons meeting, there were fewer general surgery procedures performed (P = 0.0009, -9.1% [1.9%]), fewer procedures performed of any type (P = 0.022, -2.1% [0.8]), and fewer cases of any type (P = 0.003, -2.5% [0.6%]). Similarly, during the American Academy of Orthopaedic Surgeons meeting, there were fewer joint arthroplasties performed (P < 0.0001, -27.1% [3.1%]), fewer procedures (P = 0.011, -6.3% [2.0%]), and fewer cases (P = 0.018, -5.9% [2.0%]). The results show that, in routine practice, anesthesia staffing and staff scheduling are being chosen to prevent the meeting from affecting patients' and surgeons' access to OR time.

Supporting self-regulation in simulation-based education: a randomized experiment of practice schedules and goals.

Self-regulated learning is optimized when instructional supports are provided. We evaluated three supports for self-regulated simulation-based training: practice schedules, normative comparisons, and learning goals. Participants practiced 5 endoscopy tasks on a physical simulator, then completed 4 repetitions on a virtual reality simulator. Study A compared two practice schedules: sequential (master each task in assigned order) versus unstructured (trainee-defined). Study B compared normative comparisons framed as success (10% of trainees were successful) versus failure (90% of trainees were unsuccessful). Study C compared a time-only goal (go 1min faster) versus time + quality goal (go 1min faster with better visualization and scope manipulation). Participants (18 surgery interns, 17 research fellows, 5 medical/college students) were randomly assigned to groups. In Study A, the sequential group had higher task completion (10/19 vs. 1/21; P < .001), longer persistence attempting an ultimately incomplete task (20.0 vs. 15.9min; P = .03), and higher efficiency (43% vs. 27%; P = .02), but task time was similar between groups (20.0 vs. 22.6min; P = .23). In Study B, the success orientation group had higher task completion (10/16 vs. 1/24; P < .001) and longer persistence (21.2 vs. 14.6min; P = .001), but efficiency was similar (33% vs. 35%; P = .84). In Study C, the time-only group had greater efficiency than time + quality (56% vs. 41%; P = .03), but task time did not differ significantly (172 vs. 208s; P = .07). In this complex motor task, a sequential (vs. unstructured) schedule, success (vs. failure) orientation, and time-only (vs. time + quality) goal improved some (but not all) performance outcomes.

The effect of consistent and varied follow-through practice schedules on learning a table tennis backhand

ABSTRACTIn table tennis the follow-through action after a shot is an important part of skill execution. In this experiment, we aimed to extend literature around the contextual interference effect by investigating whether the way the follow-through is organised in practice affects learning of the backhand shot in table tennis. Thirty unskilled participants were allocated to blocked-variable practice, random-variable practice or a control-constant group and aimed backhand shots towards a target following ball projection from a machine. Each group completed these shots in a pre-test, a training phase with follow-through manipulations, a post-test, and a retention test. The random-variable group improved their shot accuracy from pre-test to post-test and from pre-test to retention test (both P < 0.01, d = 1.03), whereas neither the blocked-variable nor the control-constant group displayed any change in shot accuracy. Practising the follow-through in a random-variable fashion enhanced learning of the preceding shot compared with blocked-variable practice or no follow-through instructions. The benefits of learning motor skills under conditions of high contextual interference also apply to how follow-through actions are organised. The findings are valuable to coaches and suggest that instructions related to the follow-through action should be considered as well as the primary skill itself.

Is less more? A randomized comparison of home practice time in a mind-body program

Home practice is a major component of mind-body programs, yet little is known about how to optimize the amount of prescribed home practice in order to achieve an effective “dose” of practice while minimizing participant burden. This study tested how varying the amount of home practice in a mind-body program impacts compliance and stress reduction, and whether prescribing a flexible home practice schedule increases compliance. Eighty-four stressed participants undergoing a 12-week yoga program were randomized to low, medium, and high home practice conditions. The medium condition allowed participants the flexibility to choose one of two amounts of practice each day. The low practice group exhibited the highest compliance (91%) compared to the medium and low practice groups (∼60%), but exhibited the lowest total practice time, and did not significantly reduce stress. The high practice group was the only group to achieve significant stress-reduction, which was maintained 12 weeks post program. Prescribing a flexible home practice schedule did not increase compliance. Results suggest that prescribing higher practice doses may maximize practice time and symptom reduction despite lower compliance.

Irrigation Canal System Delivery Scheduling Based on a Particle Swarm Optimization Algorithm

Reasonable planning of water delivery schedules for canal systems can reduce losses caused by water seepage and improve the utilization efficiency of irrigation water. Empirical methods of water delivery scheduling for canal systems usually cause problems such as insufficient discharge, excessively delayed water delivery, and large losses under given water requirements. In this study, a canal water delivery scheduling model was set up, and a customized algorithm based on particle swarm optimization was proposed. Typical heuristic algorithms often become trapped in local optima and often search inefficiently under numerous constraints; however, the proposed algorithm can overcome these typical problems. The proposed method was evaluated for two typical canal irrigation systems, and the results showed that the algorithm is robust and efficient and can quickly meet the water delivery optimization schedules for canal irrigation systems. Compared with empirical methods, the algorithm reduced the leakage loss of delivered water from 7.29% to 5.40%, and 8.97% to 7.46% for the two tested canal systems. The discharge of the main canal is relatively stable, which can reduce the difficulty of head gate adjustment. The proposed optimization algorithm can provide practical and efficient water delivery schedules for irrigation canal systems.

A data-driven approach for multi-objective unit commitment under hybrid uncertainties

Recent years, renewable energy has taken growing penetration in power systems due to the energy shortage and environmental concerns. As a result, system operators encounter increasing difficulties in solving unit commitment optimization. In this paper, a data-driven unit commitment model is proposed to handle the hybrid uncertainties of wind power and future load. First, a non-parameter kernel density method is utilized to represent the above hybrid uncertainties, and a novel bandwidth selection strategy for the above method is then proposed to capture the inherent correlation between uncertainty representation and unit commitment. Second, a Monte Carlo simulation is developed to integrate the hybrid uncertainties into Value-at-Risk to get a comprehensive system reliability measurement. Third, considering that system operators might be interested in the inherent conflict between reliability and economy, minimizing operation costs and maximizing system reliability are taken as two objectives in the model. To get more practical schedules, the transmission line constraint is considered as well when building the mathematical model. Additionally, by integrating the reinforcement learning mechanism, a novel multi-objective particle swarm optimization algorithm is proposed to solve the complicated nonlinear model. Finally, several experiments were performed to demonstrate the effectiveness of this research.

Effect of Blocked Versus Random Practice on Physical Therapy Students' Manual Force Modulation.

This study examined (a) the effect of practice organization on learning to modulate manual force, and (b) the effect of force magnitude on ability to accurately reproduce force. I randomly assigned 52 novice physical therapy students to either blocked or random practice schedules as they learned to apply different manual forces. I tested students immediately after training and one week later, using a three-way mixed design analysis of variance to assess the effects of practice organization (random vs. blocked), test (immediate posttest vs. retention test), and force (high vs. low) on ability to modulate manual forces. I found that blocked versus random practice, led to more accurate force application at the posttest and retention test, F(1, 50) = 5.57, p < .05; both practice groups performed more accurately at the posttest than at the retention test, F(1, 50) = 5.6, p < .05, and both performed more accurately at the lower, compared with the higher, force level, F(1, 50) = 60.9, p < .05. These findings support use of a blocked' practice schedule for this motor training, though relevant contextual interference moderators may include such variables as nature of the task, learners' skill levels, and practice time. In addition, higher forces resulted in greater errors and must be considered in preparing for and engaging in clinical practice.

Stochastic Appointment Scheduling in a Team Primary Care Practice with Two Flexible Nurses and Two Dedicated Providers

Waiting is common in appointment-based outpatient care: patients experience delays before seeing a nurse and then in the examination room before seeing the doctor. Given that service times are uncertain, the challenge for outpatient practices is to control the spacing between successive appointments to minimize waiting time while ensuring the doctor is not idle for too long. Whereas scheduling in single-doctor practices is well studied, team practice, in which one of two nurses can flexibly see the patient before the patient consults with one of two assigned doctors, is not. Optimal scheduling in a team practice is far more challenging from a computational viewpoint because the order in which patient appointments are scheduled need not be the order in which they see their doctors (i.e., patients may be allowed to crossover). In this article the authors propose a model that can solve realistic instances of this problem optimally. On the basis of analyses, the results show that (1) empty slots (which function as slack to alleviate waiting) alternate in the two doctors’ schedules and do not occur simultaneously, and (2) allowing nurse flexibility and patient crossovers produces greater benefits when service time variation is high.

Development of lossless compression algorithms for power system operational data

Power system measurement is extremely crucial for the stable power system operation and results in the generation of bulk data. This enormous volume of data must be transferred from field devices to the control centre and must be preserved for future references. Analysis of practical generation scheduling and monitoring data indicates its repetitive and slow varying nature. A simple, low-computational compression algorithm, the differential binary encoded algorithm (DBEA), is developed for compressing such information and a high compression ratio is achieved for the majority of practical data sets. To overcome the constraints of the DBEA, extended version of DBEA (E-DBEA) is developed which increases the range of input at the expense of compression ratio. Resumable load data compression algorithm (RLDA) is a differential coding-based algorithm developed for compressing load profile data. Comparison of the performance obtained by the DBEA, E-DBEA and RLDA with practical data sets clearly indicates the effectiveness of the DBEA and E-DBEA. The online test bench of the DBEA and E-DBEA consists of two inter connected PCs, one working as virtual load despatch centre and other as virtual generating station or sub-station. Due to the simplicity of the proposed work, it can be useful for data storage and data transfer both at high-level PCs and low-level microcontrollers.

Palliation or Prolongation? The Impact of a Peer-Review Intervention on Shortening Radiotherapy Schedules for Bone Metastases

Shorter fractionation radiation regimens for palliation of bone metastases result in lower financial and social costs for patients and their caregivers and have similar efficacy as longer fractionation schedules, although practice patterns in the United States show poor adoption. We investigated whether prospective peer review can increase use of shorter fractionation schedules. In June 2016, our practice mandated peer review of total dose and fractionation for all patients receiving palliative treatment during our weekly chart rounds. We used descriptive statistics and Fisher's exact test to compare lengths of treatment of uncomplicated bone metastases before and after implementation of the peer review process. Between July 2015 and December 2016, a total of 242 palliative treatment courses were delivered, including 105 courses before the peer review intervention and 137 after the intervention. We observed greater adoption of shorter fractionation regimens after the intervention. The use of 8 Gy in one fraction increased from 2.8% to 13.9% of cases postadoption. Likewise, the use of 20 Gy in five fractions increased from 25.7% to 32.8%. The use of 30 Gy in 10 fractions decreased from 55.2% to 47.4% ( P = .002), and the use of ≥ 11 fractions decreased from 16.2% before the intervention to 5.8% after ( P = .006). Prospective peer review of palliative regimens for bone metastases can lead to greater adoption of shorter palliative fractionation schedules in daily practice, in accordance with national guidelines. This simple intervention may therefore benefit patients and their caregivers as well as provide value to the health care system.

Evaluating the impact of practice conditions (randomized vs. blocked) and schedule (distributed vs. massed) on script training in aphasia

Background: A growing body of evidence suggests that intensity of therapy is a critical factor influencing outcomes in aphasia treatment regardless of the treatment approach. However, little inform...

Practical scheduling problem for sugarcane-farming corporations and its solution

Sugarcane-farming corporations manage large-scale farmlands, labor, and machinery. To efficiently manage daily operations, a systematic and detailed scheduling system is needed. Compared with other common scheduling problems, the sugarcane-farming scheduling problem presents some specific characteristics, such as cooperative work and uncertainty. By analyzing the practical constraints, we propose a new mathematical model and a hybrid approach to solve the scheduling problem of sugarcane-farming corporations. The approach comprises a meta-heuristics simulated annealing (SA) and a mixed integer programming (MIP) solver of the GNU linear programming kit. The SA algorithm is used for resource assignment and search control, whereas the MIP solver is used to acquire the optimal solution for resource assignment. For planning the farm works on large-scale-dispersed farmlands in the sugarcane-farming corporations, the hybrid approach is competent to mathematically calculate an optimal schedule with minimum completion time; this was explored from enormous candidate farm work permutations and resource combinations. The results demonstrate that the proposed hybrid approach can determine an optimal resource assignment and farm work schedule for a small-scale problem. The proposed approach is applicable to the construction of a long-term detailed scheduling system for sugarcane-farming corporations.

A correction method for HVDC transmission plan considering the correlation between sending end new energy generation and receiving end loads

Recently, new energy is conversely distributed and centralized grid connection which need transmit redundant power to other regions urgently by HVDC system. Based on the characteristics of new energy, this paper analyzes the correlation between sending end new energy and receiving end loads by person correlation coefficient method and proposes a correction method for HVDC transmission plan considering the correlation which promotes new energy penetration level and improves utilization of HVDC system. By taking a HVDC system as an example, the results of analyzing practical scheduling data in 2015 demonstrate the feasibility of the proposed method.

Toxicity and Outcomes Associated with High-Dose Rate Brachytherapy for Medically Inoperable Endometrial Cancer

Surgery is the standard of care for early stage endometrial cancer. However, some patients are deemed medically inoperable due to comorbid medical conditions. For these patients, definitive radiation in the form of intracavitary brachytherapy (BT) with or without external beam radiation (EBRT) can be an effective alternative. In clinical practice, various doses and fractionation schedules of BT are used without evidence on the optimal schedule. The primary aim of this study was to evaluate treatment related toxicity in medically inoperable endometrial cancer treated with definitive radiation therapy.

Effects of implicit and explicit practice schedule on performanc and learning of bimanual coordination task of elder

Effects of implicit and explicit practice schedule on performanc and learning of bimanual coordination task of elder